MétaCan
Menu
Back to cohort
Record W2783993306 · doi:10.1139/cjfr-2017-0381

A test of the predator satiation hypothesis, acorn predator size, and acorn preference

2018· article· en· W2783993306 on OpenAlexvenueno aff
Cathryn H. Greenberg, Stanley J. Zarnoch

Bibliographic record

VenueCanadian Journal of Forest Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAcornBiologyPredatorPredationExclosureSeed predationEcologyCropHerbivoreSeed dispersalPopulation

Abstract

fetched live from OpenAlex

Mast seeding is hypothesized to satiate seed predators with heavy production and reduce populations with crop failure, thereby increasing seed survival. Preference for red or white oak acorns could influence recruitment among oak species. We tested the predator satiation hypothesis, acorn preference, and predator size by concurrently measuring acorn production, mouse abundance, and white versus red oak acorn removal rates in exclosures allowing access by mice (HW), squirrels and smaller-sized vertebrates (WW), or all-sized vertebrates (C) for 12 years. Annual removal rate varied, but virtually all acorns were eventually removed from all exclosure types all years except one. Acorns were removed more slowly from HW than from WW or C exclosures, indicating that large vertebrates were not major acorn consumers, locally. Red and white oak acorn removal rates were similar except in two years, when red oak acorns were removed more rapidly. Removal slowed with increasing acorn crops, suggesting that heavy crops can “swamp” predators. Removal rate was negatively correlated with crop size the previous fall. A positive trend between mouse abundance and crop size the previous fall was evident; abundance decreased sharply the year following crop failures but not after moderate or heavy crops, suggesting that poor crops can dampen acorn predation the following year.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.293
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicAnimal Ecology and Behavior StudiesFrench-language works237,207